Quick Positional Health Assessment for Industrial Robot Prognostics and Health Management (PHM)
Guixiu Qiao, Craig I. Schlenoff, Brian A. Weiss
Robot calibration and performance will degrade if proper maintenance isn't performed. There have been challenges for manufacturers to optimize the maintenance strategy and minimize unexpected shutdowns. Prognostics and health management (PHM) can be applied to industrial robots through the development of performance metrics, test methods, reference datasets, and supporting tools. A subset of this research involves developing a quick health assessment methodology emphasizing the identification of static and dynamic positional accuracies. This methodology enables manufacturers to quickly assess the positional health of their robot systems. In this paper, the National Institute of Standards and Technology's (NIST) effort to develop the measurement science to support this development is presented, including the modeling and algorithm development for the test method, the advanced sensor development to measure 7-D information (time, X, Y, Z, roll, pitch, and yaw), algorithms to analyze the data, and a use case to present the results.
IEEE International Conference on Robotics and Automation 2017
, Schlenoff, C.
and Weiss, B.
Quick Positional Health Assessment for Industrial Robot Prognostics and Health Management (PHM), IEEE International Conference on Robotics and Automation 2017, -, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=922115
(Accessed September 23, 2021)